Classification-based method and apparatus for string selectivity estimation

ABSTRACT

Histogram construction and selectivity estimation for string and substring match queries in databases of data having strings associated with attributes. The histogram construction counts string-attribute pairs in the documents, and outputs string-attribute-count triples sorted by count. The collection is partitions the collection into buckets. A synopsis is generated for the partition, having an average selectivity or count of the string-attribute-count triples in the partition and summary information representing the set of string-attribute pairs belonging to the bucket. Subsequent queries, both for exact and substring matches, use the synopsis to estimate the selectivity of buckets.

FIELD OF THE INVENTION

The field of the invention is data processing, more specifically, a system and method for collecting and maintaining statistics and generating selectivity estimation for string and substring match queries in databases of any data having strings associated with attributes.

BACKGROUND OF THE INVENTION

XML is an example of a database of strings associated with attributes, and in native database management systems (“DBMSs”) of, for example, XML documents, the documents are stored as trees and queries are processed using either indexes or tree traversals. Nodes in a XML tree are often located using paths.

A path is a sequence of tag names that specify a navigational trajectory to a set of nodes in the XML tree. A rooted path is one that begins at the root node of the XML tree. For efficient processing, complex path expressions in XML queries are often preprocessed into a set of candidate (path, pred) pairs, where “path” is a linear rooted path and “pred” is a string predicate on the leaf value reachable via path. Consequently, an XML query (such as XQuery) can be mapped to several retrieval operations using (path, pred) pairs. These retrieval operations using (path, pred) pairs form the set of basic query processing operators. Accurate estimation of the selectivity of such (path, pred) pairs is therefore crucial for choosing an optimal execution plan in cost-based query optimization. Previous work on XML selectivity estimation has focused mainly on the tag-labeled paths (tree structure) of the XML data. Examples of this previous work are: A. Aboulnaga, et al., “Estimating the selectivity of XML path expressions for internet scale applications,” VLDB 2001, pp. 591-600, (2001), N. Polyzotis, et al., “Statistical synopses for graph-structured XML databases,” SIGMOD 2002, pp. 358-369, (2002).

However, for most actually existing attribute-string databases, such as XML, the number of distinct string values at the leaf nodes is orders of magnitude larger than the set of distinct rooted tag paths. A significant challenge therefore exists in accurately estimating the selectivity of string predicates on the leaf values reachable via a given path.

Using XML as an example, the XML string selectivity estimation problem is defined as follows: Given a (path, pred) query, where pred is a string predicate, estimate the number of nodes in the XML data that are reachable by path and whose associated string values satisfy the string predicate pred. Examples of queries with different string predicates include exact match queries and substring queries. An exact match query is specified by a (path, string) pair and retrieves all nodes reachable via path whose string value matches the query string exactly. A substring query is specified by a (path, substring) pair and retrieves all nodes reachable via path whose string value contains substring.

The present invention provides a novel method for collecting and maintaining statistics to support selectivity estimation for both exact/full string match queries and substring match queries of any database of documents arranged data strings associated with any number of attributes. In the case of XML, the data consists of path-string pairs, where each string is associated with exactly one attribute that is a path. In other cases, each string can be associated with any number (including zero) of attributes.

SUMMARY OF THE INVENTION

Given a set of documents in a language comprising strings associated with attributes, such as XML, a partition-based histogram is generated that is much smaller in size than the original data set. The generated histogram can support, for example, selectivity estimation for both exact/full string match queries and substring match queries in such string databases accurately.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an importance of the selectivity estimation in a cost-based query optimizer of a database management system;

FIG. 2 illustrates the definition of string selectivity using an exeplary XML document;

FIG. 3 illustrates how the partition-based histogram used by an exemplary embodiment of the present invention is constructed from the exemplary XML data; and

FIG. 4 illustrates the procedure for estimating the selectivity for both exact string match queries and substring match queries using the partition-based histogram according to the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

FIG. 1 illustrates the importance of the selectivity estimation, of which the present invention is a solution, in a cost-based query optimizer of a database management system. Block 110 denotes a user query expressed in some declarative query language such, as for example, Structured Query Language (SQL). Block 120 denotes a plan enumeration process that takes as input the query 110 and outputs a collection of query execution plans 130, each of which will generate the correct result. The query optimizer 140 uses the selectivity estimator 150 to evaluate each of the query execution plans 130 in order to output the most efficient plan 170. The selectivity estimator 150 requires statistics 160 in order to return selectivity estimates to the query optimizer 140. The chosen query execution plan 170 is then executed by the query execution engine 180, and the query result 190 is returned to the user.

FIG. 2 illustrates the definition of string selectivity in a database of string-attribute documents, such as XLM, using an exemplary XML document denoted by 210. It will be understood that XML is used only as example string-attribute language for purposes of describing the present invention, as XML is well known and, therefore, the theory, operation and implementation of the invention can be easily understood from this description and present invention readily applied, by persons skill in the relevant art, to any data where strings in the data are associated with any number of attributes. In the case of XML, the data consists of path-string pairs, where each string is associated with exactly one attribute that is a path. In other cases, each string can be associated with any number (including zero) of attributes.

Referring again to FIG. 2, block 220 shows the selectivity of all of the exact string match path-value pairs entailed by the exemplary XML document of 210. For example, the exact match query (/A/B, abab) on the exemplary XML document in FIG. 2 has a selectivity of 10, and the substring query (/A/B, *bba*) has a selectivity of 9.

The current invention can be used to estimate the selectivity for both exact/full string match queries and substring match queries in, for example, XML databases. It can be described as being composed of two parts: histogram construction and selectivity estimation.

Referring now to FIG. 3, the histogram construction procedure for an exemplary embodiment of the current invention is illustrated. The histogram construction procedure consists of three steps, labeled 320, 340, and 360, respectively. Step 320 counts each path-value pair in the example XML document 310 and outputs the collection of path-value-count triples sorted by count 330. Step 340 partitions the collection of sorted path-value-count triples 330 using a partitioning algorithm such as, for example, MaxDiff, LloydMax or equivalents.

The output of the partitioning algorithm is a collection of partitions, also referred to as “buckets”, of path-value-count triples 350. In this example, two partitions or buckets are used. The generate synopsis step 360 extracts a synopsis for each partition. The exemplary synopsis for each partition consists of the average selectivity or count of all the path-value-count triples in the partition and some summary information that represents the set of path-value pairs belonging to this partition.

In the preferred embodiment of the current invention, the synopsis in each bucket, where the bucket identifier is BKT, consists of two types of counts:

-   -   1) Occurrence counts of paths that are associated with the         bucket     -   2) Occurrence counts of the q-grams of the values that are         associated with the bucket. For example, the 2-grams of the         value “abbba” are “ab,bb,bb,ba”.         The paths and the q-grams on the values are counted         independently.

Referring now to FIG. 4, the procedure for estimating the selectivity of exact and sub-string match queries according to the current invention is illustrated. Bock 410 denotes the input query Q that can either be an exact match query or a substring match query. The step 420 checks the type of the input query: if the input query is an exact match query, steps 430-440 are taken, and if the input query is a substring match query, steps 450-460 are taken. For exact match queries, step 430 computes the a posterior probability of each bucket given the query, using the synopsis in the histogram from FIG. 3, and finds the bucket bkt that maximizes the a posteriori probability.

Step 440 extracts the average selectivity sel(BKT) of bucket BKT from the histogram and returns it. For substring match queries, step 450 computes for each bucket b, the estimated number of times N(BKT) the substring query occurs in the bucket BKT. Step 460 uses these occurrence counts or frequencies to compute a weighted sum of the frequency N(BKT) and the average selectivity sel(BKT) of the bucket BKT. The weighted sum is returned as the selectivity estimate.

For example, using the exemplary histogram computed in FIG. 3 by the exemplary embodiment of the current invention on the exact match query /A/B=abab, the probabilities computed by step 430 are:

$\begin{matrix} {{P\left( {B = {{0{{\text{❘}/A}/B}} = {abab}}} \right)} = {C*{P\left( {{{{/A}/B}\text{❘}B} = 0} \right)}*{P\left( {{{ab}\text{❘}B} = 0} \right)}*}} \\ {{{P\left( {{{ba}\text{❘}B} = 0} \right)} \div {P\left( {{b\text{❘}B} = 0} \right)}}*{{P\left( {{{ab}\text{|}B} = 0} \right)} \div}} \\ {{P\left( {{a\text{❘}B} = 0} \right)}*{P\left( {B = 0} \right)}} \\ {= {C*1*{2/5}*{{{2/5} \div 3}/5}*{{2/5} \div 1}*{2/5}}} \\ {= {0.0427*C}} \end{matrix}$ $\begin{matrix} {{P\left( {B = {{1{{\text{❘}/A}/B}} = {abab}}} \right)} = {C*{P\left( {{{{/A}/B}\text{❘}B} = 1} \right)}*{P\left( {{{ab}\text{❘}B} = 1} \right)}*}} \\ {{{P\left( {{{ba}\text{❘}B} = 1} \right)} \div {P\left( {{b\text{❘}B} = 1} \right)}}*{{P\left( {{{ab}\text{|}B} = 1} \right)} \div}} \\ {{P\left( {{a\text{❘}B} = 1} \right)}*{P\left( {B = 1} \right)}} \\ {= {C*{1/3}*{3/9}*{{{3/9} \div 5}/9}*{{{3/9} \div 4}/9}*{3/5}}} \\ {{= {0.03*C}},} \end{matrix}$ where  C  is  a  constant, common  to  both  probability  terms. C is derivable using, for example, Bayes's Rule, as follows:

${{P\left( {B = {{b\text{❘}A} = a}} \right)} = {C*{P\left( {A = {{a\text{|}B} = b}} \right)}*{{P\left( {B = b} \right)}.{From}}\mspace{14mu}{{Bayes}'}s\mspace{14mu}{Rule}}},\begin{matrix} {{P\left( {B\text{❘}A} \right)} = {{P\left( {A,B} \right)}/{P(A)}}} \\ {= {{P\left( {A\text{❘}B} \right)}*{{P(B)}/{P(A)}}}} \\ {= {{P\left( {A❘B} \right)}*{{P(B)}/{\left\lbrack {{Sum}\mspace{14mu}{over}\mspace{14mu}{all}\mspace{14mu} B\mspace{11mu}{P\left( {A,B} \right)}} \right\rbrack.}}}} \end{matrix}$

-   -   Therefore C=1/[Sum over all B P(A,B)] which is independent of         the value of B.

Step 430 then determines that bucket 0 maximizes the probability. Step 440 returns the average selectivity of bucket 0, that is 9, as the estimate. For the substring match query /A/B=*bba*, step 450 computes the following frequencies,

$\begin{matrix} {N\left( {{{/A}/B} = {{{*{bba}*}❘B} = 0}} \right)} \\ {= {{N\left( {{{bb}\text{❘}B} = 0} \right)}*{{N\left( {{{ba}\text{❘}B} = 0} \right)} \div {N\left( {{b\text{|}B} = 0} \right)}}}} \\ {= {{1*{2 \div 3}} = {2/3}}} \end{matrix}$ $\begin{matrix} {N\left( {{{/A}/B} = {{*{bba}*\text{❘}B} = 1}} \right)} \\ {= {{N\left( {{{bb}\text{❘}B} = 1} \right)}*{{N\left( {{{ba}\text{❘}B} = 1} \right)} \div {N\left( {{b\text{❘}B} = 1} \right)}}}} \\ {{= {{2*{3 \div 5}} = {6/5}}},} \end{matrix}$ and step 460 computes the estimate as the weighted sum,

$\begin{matrix} {\begin{matrix} \begin{matrix} \begin{matrix} {N\left( {{{/A}/B} = {{*{bba}*\text{❘}B} = 0}} \right)*} \\ {{{Average}\mspace{14mu}{selectivity}\mspace{14mu}{of}\mspace{14mu}{bucket}\mspace{14mu} 0} +} \end{matrix} \\ {N\left( {{{/A}/B} = {{*{bba}*\text{❘}B} = 1}} \right)*} \end{matrix} \\ {{Average}\mspace{14mu}{selectivity}\mspace{14mu}{of}\mspace{14mu}{bucket}\mspace{14mu} 1} \end{matrix} = {{{2/3}*9} + {{6/5}*1.333}}} \\ {= 7.6} \end{matrix}$

While certain embodiments and features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents will occur to those of ordinary skill in the art. It is therefore to be understood that the appended claims are intended to cover all such modifications and changes as fall within the spirit of the invention. 

1. A classification-based method for string selectivity estimation in an XML database, comprising: (1) from an original data set including XML documents, constructing a partition-based histogram that is smaller in size than the original data set, the histogram-constructing comprising: (A) in an XML document, counting each path-value pair and outputting a collection of path-value-count triples sorted by count; (B) partitioning the collection of sorted path-value-count triples by applying a partitioning algorithm to provide a collection of partitions of path-value-count triples, each partition being a bucket; (C) for each partition, extracting a synopsis wherein the synopsis for each partition consists of average selectivity or count of all the path-value-count triples in the partitions and summary information that represents a set of path-value pairs belonging to the partition for which the synopsis is being extracted, further provided that the synopsis in each bucket consists of (i) occurrence counts of path associated with the bucket and (ii) occurrence counts of q-grams of values associated with the bucket, including: independently counting the paths and the q-grams on the values; provided that the histogram-constructing includes providing a memory limit value and fitting the histogram entirely within the memory limit value; and (2) applying the histogram to estimate selectivity for both exact/full string match queries and substring match queries in an XML database, comprising: receiving a match query that is an exact match query or a substring match query; when the query is an exact match query: computing an a posterior probability of each bucket given the input query, applying the synopsis in the histogram and finding the bucket that maximizes the a posteriori probability; from the histogram, for the bucket extracting an average selectivity and returning the average selectivity; when the query is a substring match query: computing for each bucket, an estimated number of times that the substring query occurs in the bucket to produce occurrence counts or frequencies from which to compute a weighted sum of frequency and average selectivity of the bucket, and returning the weighted sum as a selectivity estimate. 